no code implementations • 5 Feb 2024 • Juncai He, Liangchen Liu, Yen-Hsi Richard Tsai
This paper investigates the impact of multiscale data on machine learning algorithms, particularly in the context of deep learning.
no code implementations • 20 Dec 2023 • Xingyilang Yin, Xi Yang, Liangchen Liu, Nannan Wang, Xinbo Gao
Additional offsets and modulation scalars are learned on the whole point features, which shift the deformable reference points to the regions of interest.
no code implementations • 22 Nov 2023 • Andrew M. Nguyen, Tejas Sudharshan Mathai, Liangchen Liu, Jianfei Liu, Ronald M. Summers
In this pilot work, we developed a fully automated approach for the measurement of PCAT mean attenuation and volume in the region around both coronary arteries.
no code implementations • 14 Sep 2023 • Liangchen Liu, Nannan Wang, Dawei Zhou, Xinbo Gao, Decheng Liu, Xi Yang, Tongliang Liu
This paper targets a novel trade-off problem in generalizable prompt learning for vision-language models (VLM), i. e., improving the performance on unseen classes while maintaining the performance on seen classes.
no code implementations • 6 Sep 2023 • Mengliang Zhang, Xinyue Hu, Lin Gu, Liangchen Liu, Kazuma Kobayashi, Tatsuya Harada, Ronald M. Summers, Yingying Zhu
In this paper, we re-extract disease labels from CXR reports to make them more realistic by considering disease severity and uncertainty in classification.
1 code implementation • 22 Jul 2023 • Xinyue Hu, Lin Gu, Qiyuan An, Mengliang Zhang, Liangchen Liu, Kazuma Kobayashi, Tatsuya Harada, Ronald M. Summers, Yingying Zhu
Given a pair of main and reference images, this task attempts to answer several questions on both diseases and, more importantly, the differences between them.
no code implementations • 5 Jul 2023 • Liangchen Liu, Juncai He, Richard Tsai
We assume that the data manifold is smooth and is embedded in a Euclidean space, and our objective is to reveal the impact of the data manifold's extrinsic geometry on the regression.
no code implementations • 3 Nov 2022 • Liangchen Liu, Qiuhong Ke, Chaojie Li, Feiping Nie, Yingying Zhu
In this paper, we formulate a novel clustering model, which exploits the non-negative feature property and, more importantly, incorporates the multi-view information into a unified joint learning framework: the unified multi-view orthonormal non-negative graph based clustering framework (Umv-ONGC).
no code implementations • 13 Aug 2022 • Xinyue Hu, Lin Gu, Liangchen Liu, Ruijiang Li, Chang Su, Tatsuya Harada, Yingying Zhu
Existing video domain adaption (DA) methods need to store all temporal combinations of video frames or pair the source and target videos, which are memory cost expensive and can't scale up to long videos.
no code implementations • 25 Nov 2019 • Liangchen Liu, Louis Ly, Colin Macdonald, Yen-Hsi Richard Tsai
We propose a new framework for the sampling, compression, and analysis of distributions of point sets and other geometric objects embedded in Euclidean spaces.
no code implementations • 27 Sep 2019 • Jingwei Ma, Jiahui Wen, Mingyang Zhong, Liangchen Liu, Chaojie Li, Weitong Chen, Yin Yang, Honghui Tu, Xue Li
In addition, we propose to jointly learn user-user group (item-item group) hierarchies, so that we can effectively discover latent groups and learn compact user/item representations.
no code implementations • 16 Jul 2019 • Liangchen Liu, Teng Zhang, Kun Zhao, Arnold Wiliem, Kieren Astin-Walmsley, Brian Lovell
We propose a novel two-stage zoom-in detection method to gradually focus on the object of interest.
no code implementations • 17 Oct 2016 • Liangchen Liu, Arnold Wiliem, Shaokang Chen, Brian C. Lovell
With this metric, automatic quantitative evaluation can be performed on the attribute sets; thus, reducing the enormous effort to perform manual evaluation.
no code implementations • 21 Feb 2016 • Liangchen Liu, Arnold Wiliem, Shaokang Chen, Kun Zhao, Brian C. Lovell
In this paper, we propose a novel approach, based on the shared structure exhibited amongst meaningful attributes, that enables us to compare between different automatic attribute discovery approaches. We then validate our approach by comparing various attribute discovery methods such as PiCoDeS on two attribute datasets.
no code implementations • 5 Feb 2016 • Liangchen Liu, Arnold Wiliem, Shaokang Chen, Brian C. Lovell
In our evaluation, we gleaned some insights that could be beneficial in developing automatic attribute discovery methods to generate meaningful attributes.